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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gII_M28Gpden",
        "outputId": "f017b6dc-dc7b-4edc-ed38-2f2f73cbe07e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m106.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m76.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%env XLA_PYTHON_CLIENT_PREALLOCATE=false"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6CKu-iBnFbz",
        "outputId": "b92cf5b8-212a-45de-d396-7536a6fc150c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: XLA_PYTHON_CLIENT_PREALLOCATE=false\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%env XLA_PYTHON_CLIENT_ALLOCATOR=platform"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08dC1C88o-ej",
        "outputId": "9cee1baf-ebe8-4cd6-e284-2a5852343839"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: XLA_PYTHON_CLIENT_ALLOCATOR=platform\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import FlaxAutoModelForCausalLM, AutoTokenizer\n",
        "import jax"
      ],
      "metadata": {
        "id": "5Swbwy4xqSEq"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import FlaxGPTJForCausalLM #?"
      ],
      "metadata": {
        "id": "BZkJsfT26Epx"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"EleutherAI/gpt-j-6B\"\n",
        "#model_name = \"EleutherAI/gpt-neo-125M\"\n",
        "#model_name = \"EleutherAI/gpt-neo-1.3B\"\n",
        "#model_name = \"EleutherAI/gpt-neo-2.7B\""
      ],
      "metadata": {
        "id": "cFQQ9KyQ7hmZ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = FlaxAutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    revision=\"float16\",\n",
        "    dtype=jax.numpy.float16,\n",
        "    from_pt=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205,
          "referenced_widgets": [
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/float16/config.json:   0%|          | 0.00/836 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/12.1G [00:00<?, ?B/s]"
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at EleutherAI/gpt-j-6B were not used when initializing FlaxGPTJForCausalLM: {('transformer', 'h', '9', 'attn', 'bias'), ('transformer', 'h', '4', 'attn', 'masked_bias'), ('transformer', 'h', '6', 'attn', 'masked_bias'), ('transformer', 'h', '5', 'attn', 'bias'), ('transformer', 'h', '26', 'attn', 'bias'), ('transformer', 'h', '21', 'attn', 'bias'), ('transformer', 'h', '27', 'attn', 'masked_bias'), ('transformer', 'h', '13', 'attn', 'bias'), ('transformer', 'h', '9', 'attn', 'masked_bias'), ('transformer', 'h', '5', 'attn', 'masked_bias'), ('transformer', 'h', '20', 'attn', 'masked_bias'), ('transformer', 'h', '26', 'attn', 'masked_bias'), ('transformer', 'h', '21', 'attn', 'masked_bias'), ('transformer', 'h', '16', 'attn', 'bias'), ('transformer', 'h', '13', 'attn', 'masked_bias'), ('transformer', 'h', '8', 'attn', 'bias'), ('transformer', 'h', '14', 'attn', 'bias'), ('transformer', 'h', '19', 'attn', 'bias'), ('transformer', 'h', '3', 'attn', 'bias'), ('transformer', 'h', '23', 'attn', 'bias'), ('transformer', 'h', '17', 'attn', 'bias'), ('transformer', 'h', '16', 'attn', 'masked_bias'), ('transformer', 'h', '8', 'attn', 'masked_bias'), ('transformer', 'h', '14', 'attn', 'masked_bias'), ('transformer', 'h', '0', 'attn', 'bias'), ('transformer', 'h', '24', 'attn', 'bias'), ('transformer', 'h', '18', 'attn', 'bias'), ('transformer', 'h', '23', 'attn', 'masked_bias'), ('transformer', 'h', '17', 'attn', 'masked_bias'), ('transformer', 'h', '19', 'attn', 'masked_bias'), ('transformer', 'h', '3', 'attn', 'masked_bias'), ('transformer', 'h', '0', 'attn', 'masked_bias'), ('transformer', 'h', '22', 'attn', 'bias'), ('transformer', 'h', '24', 'attn', 'masked_bias'), ('transformer', 'h', '18', 'attn', 'masked_bias'), ('transformer', 'h', '10', 'attn', 'bias'), ('transformer', 'h', '1', 'attn', 'bias'), ('transformer', 'h', '7', 'attn', 'bias'), ('transformer', 'h', '22', 'attn', 'masked_bias'), ('transformer', 'h', '2', 'attn', 'bias'), ('transformer', 'h', '11', 'attn', 'bias'), ('transformer', 'h', '12', 'attn', 'bias'), ('transformer', 'h', '15', 'attn', 'bias'), ('transformer', 'h', '1', 'attn', 'masked_bias'), ('transformer', 'h', '10', 'attn', 'masked_bias'), ('transformer', 'h', '11', 'attn', 'masked_bias'), ('transformer', 'h', '7', 'attn', 'masked_bias'), ('transformer', 'h', '15', 'attn', 'masked_bias'), ('transformer', 'h', '25', 'attn', 'bias'), ('transformer', 'h', '2', 'attn', 'masked_bias'), ('transformer', 'h', '6', 'attn', 'bias'), ('transformer', 'h', '12', 'attn', 'masked_bias'), ('transformer', 'h', '4', 'attn', 'bias'), ('transformer', 'h', '27', 'attn', 'bias'), ('transformer', 'h', '25', 'attn', 'masked_bias'), ('transformer', 'h', '20', 'attn', 'bias')}\n",
            "- This IS expected if you are initializing FlaxGPTJForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing FlaxGPTJForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some of the weights of FlaxGPTJForCausalLM were initialized in float16 precision from the model checkpoint at EleutherAI/gpt-j-6B:\n",
            "[('lm_head', 'bias'), ('lm_head', 'kernel'), ('transformer', 'h', '0', 'attn', 'k_proj', 'kernel'), ('transformer', 'h', '0', 'attn', 'out_proj', 'kernel'), ('transformer', 'h', '0', 'attn', 'q_proj', 'kernel'), ('transformer', 'h', '0', 'attn', 'v_proj', 'kernel'), ('transformer', 'h', '0', 'ln_1', 'bias'), ('transformer', 'h', '0', 'ln_1', 'scale'), ('transformer', 'h', '0', 'mlp', 'fc_in', 'bias'), ('transformer', 'h', '0', 'mlp', 'fc_in', 'kernel'), ('transformer', 'h', '0', 'mlp', 'fc_out', 'bias'), ('transformer', 'h', '0', 'mlp', 'fc_out', 'kernel'), ('transformer', 'h', '1', 'attn', 'k_proj', 'kernel'), ('transformer', 'h', '1', 'attn', 'out_proj', 'kernel'), ('transformer', 'h', '1', 'attn', 'q_proj', 'kernel'), ('transformer', 'h', '1', 'attn', 'v_proj', 'kernel'), ('transformer', 'h', '1', 'ln_1', 'bias'), ('transformer', 'h', '1', 'ln_1', 'scale'), ('transformer', 'h', '1', 'mlp', 'fc_in', 'bias'), ('transformer', 'h', '1', 'mlp', 'fc_in', 'kernel'), 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            "You should probably UPCAST the model weights to float32 if this was not intended. See [`~FlaxPreTrainedModel.to_fp32`] for further information on how to do this.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
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        },
        "id": "nyTbq0rK7fdx",
        "outputId": "9113a87d-a6c4-4b1a-8cfd-862e0b386a1f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/619 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3888d8b05cb4695b616b995d0fa086a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "181298648e2b432d888ce0008ad900e6"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "582cd5b215b04db7b93974a6460661bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.37M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0601e24f7664804801deb6da6a87990"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/4.04k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8cc2e20e26584cceaffda3e6177c715b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/357 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2fafd0417dd4ae0b5afb525f81ee9fd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi --query-gpu=utilization.memory,memory.total,memory.free,memory.used --format=csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OHh1QgftmRmz",
        "outputId": "9763d6cb-8ad8-4745-feba-ced09f4292d4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "utilization.memory [%], memory.total [MiB], memory.free [MiB], memory.used [MiB]\n",
            "0 %, 40960 MiB, 28052 MiB, 12461 MiB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9M22I3o7luJW",
        "outputId": "68d66f06-a91f-4410-c8d5-da4f5fcc1603"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Sep  2 09:28:11 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   31C    P0    53W / 350W |  12461MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16\n",
        "# or bf16\n",
        "model.params = model.to_fp16(model.params)"
      ],
      "metadata": {
        "id": "DL6sQjk7q6zX"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi --query-gpu=utilization.memory,memory.total,memory.free,memory.used --format=csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8xshtDF7PLi",
        "outputId": "fde2f4be-233d-4643-e223-b59b0def7e4b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "utilization.memory [%], memory.total [MiB], memory.free [MiB], memory.used [MiB]\n",
            "0 %, 40960 MiB, 28052 MiB, 12461 MiB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVyCKzh_hJ-x",
        "outputId": "6e75210b-290b-4e0f-af79-b6248181baac"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<transformers.models.gptj.modeling_flax_gptj.FlaxGPTJForCausalLM at 0x7c06953b3670>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yL3W4SxFhMa3",
        "outputId": "3707ac9d-4862-481f-f9b4-50d05bf2da8e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2TokenizerFast(name_or_path='EleutherAI/gpt-j-6B', vocab_size=50257, model_max_length=2048, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True)}, clean_up_tokenization_spaces=True)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"Generate SQL query from the text inside triple backticks\n",
        "```select all the users from table 'users' older than 20 years```\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "JWz4YkuSrZuR"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(prompt, return_tensors=\"jax\")"
      ],
      "metadata": {
        "id": "RLtfdjSERxlg"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_yqHR0yUfjZ",
        "outputId": "3ebbcba7-2d35-4e9b-8970-940af99a8bee"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': Array([[ 8645,   378, 16363, 12405,   422,   262,  2420,  2641, 15055,\n",
              "          736,    83,  3378,   198, 15506,    63, 19738,   477,   262,\n",
              "         2985,   422,  3084,   705, 18417,     6,  4697,   621,  1160,\n",
              "          812, 15506,    63,   198]], dtype=int32), 'attention_mask': Array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://huggingface.co/blog/how-to-generate\n",
        "\n",
        "https://huggingface.co/docs/transformers/main_classes/text_generation#generation\n",
        "\n",
        "https://huggingface.co/docs/transformers/generation_strategies\n",
        "\n"
      ],
      "metadata": {
        "id": "GnuRxBPtecMG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "generated_ids = model.generate(\n",
        "    **inputs,\n",
        "    do_sample=True,\n",
        "    num_beams=1,\n",
        "    max_new_tokens=300,\n",
        "    temperature=0.7,\n",
        "    pad_token_id = model.config.eos_token_id,\n",
        "    prng_key=jax.random.PRNGKey(4232),\n",
        "    no_repeat_ngram_size=2)"
      ],
      "metadata": {
        "id": "tg8SRDAHX5yD"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generated_text = tokenizer.decode(generated_ids['sequences'].squeeze(0))\n",
        "print(generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJrFj-pMWehb",
        "outputId": "21b28900-50ba-4d9b-a079-f24ca83880fd"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generate SQL query from the text inside triple backticks\n",
            "```select all the users from table 'users' older than 20 years```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE age > 20\n",
            "```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE age > `20 years`\n",
            "```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE `age` > 20`\n",
            "```\n",
            "\n",
            "<!--end-query-->\n",
            "\n",
            "## Generate SQL query from the text inside double backticks\n",
            "```select all the users from table `users` older than 20 years```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE `age` > 20\n",
            "```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE `age` > `20 years`\n",
            "```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE `age` > 20`\n",
            "```\n",
            "\n",
            "<!--end-query-->\n",
            "\n",
            "## Generate SQL query from the text inside single backticks\n",
            "```select all the users from table `users` older than 20 years```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE `age` > 20\n",
            "```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE age > `20 years`\n",
            "```\n",
            "\n",
            "```\n",
            "SELECT * FROM `users` WHERE `age` > 20`\n",
            "```\n",
            "\n",
            "<!--end-query-->\n",
            "\n",
            "## Generate SQL query from the text inside single backticks with escaped quotes\n",
            "```select all the users\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6rNc8VXyREeu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}